Evaluating the Robustness of In-Context Learning in Large Language Models: A Syntactic Generalization Study

Alapfogalmak

Large language models can learn new tasks through in-context learning, but their ability to generalize beyond the provided examples in a robust, syntax-aware manner is limited. Models pre-trained on code demonstrate better out-of-distribution generalization compared to those trained only on natural language.

Kivonat

The authors investigate the robustness of in-context learning (ICL) in large language models (LLMs) using syntactic transformation tasks and natural language inference. They find that while LLMs can learn the tasks well on in-distribution examples, their ability to generalize to out-of-distribution examples that require syntactic reasoning varies greatly across models.

Key highlights:

• Models pre-trained on a significant amount of code (e.g., GPT-3.5 code-davinci-002, CodeLlama) demonstrate better out-of-distribution generalization compared to models trained primarily on natural language.
• Chain-of-thought prompting can improve in-distribution performance but often decreases out-of-distribution performance, underscoring the importance of evaluating beyond just in-distribution examples.
• Reinforcement learning on human feedback (as in GPT-3.5 text-davinci-003) may harm a model's ability to generalize robustly, in contrast to fine-tuning on human demonstrations.
• The authors find a strong correlation between a model's reasoning accuracy, faithfulness to its own reasoning, and out-of-distribution performance, suggesting that better syntactic reasoning is key to robust generalization.

Overall, the results indicate that scale alone is not sufficient for LLMs to acquire robust syntactic understanding, and that the pre-training corpus and supervision methods play a crucial role in determining generalization capabilities.

Statisztikák

"We further investigate whether out-of-distribution generalization can be improved via chain-of-thought prompting, where the model is provided with a sequence of intermediate computation steps that illustrate how the task ought to be performed."
"We find large variance across LMs. The variance is explained more by the composition of the pre-training corpus and supervision methods than by model size; in particular, models pre-trained on code generalize better, and benefit more from chain-of-thought prompting."
"On average, scores are higher given any prompt for models pre-trained on code: CodeLlama significantly outperforms Llama 2 on question formation (and performs comparably on tense reinflection), while GPT-3.5 code-davinci-002 outperforms all other GPT-3 and GPT-3.5 models."
"GPT-3.5 text-davinci-003 performs at least as well as other GPT-3.5 models on in-distribution examples, but it generalizes consistently worse than other models which are fine-tuned on human demonstrations (including text-davinci-002 and CodeLlama)."

Idézetek

"In-context learning (ICL) is now a common method for teaching large language models (LLMs) new tasks: given labeled examples in the input context, the LLM learns to perform the task without weight updates."
"Do models guided via ICL infer the underlying structure of the task defined by the context, or do they rely on superficial heuristics that only generalize to identically distributed examples?"
"We find large variance across LMs. The variance is explained more by the composition of the pre-training corpus and supervision methods than by model size; in particular, models pre-trained on code generalize better, and benefit more from chain-of-thought prompting."

Főbb Kivonatok

In-context Learning Generalizes, But Not Always Robustly

by Aaron Muelle... : arxiv.org 04-11-2024

https://arxiv.org/pdf/2311.07811.pdf

Mélyebb kérdések

How might the findings on the impact of code pre-training and reinforcement learning on human feedback extend to other fundamental linguistic abilities beyond syntax, such as semantics, pragmatics, or discourse structure?

The findings on the impact of code pre-training and reinforcement learning on human feedback in the context of syntax can potentially extend to other fundamental linguistic abilities beyond syntax. For example:

Semantics: Code pre-training, which involves exposure to structured programming languages, may help LLMs develop a better understanding of semantic relationships within language. By learning to interpret and generate code, models may also improve their ability to understand the meaning and context of words and sentences. This exposure to structured data could enhance semantic reasoning and the ability to capture nuanced meanings in text.

Pragmatics: Understanding pragmatics, which involves the study of how context influences the interpretation of language, could benefit from code pre-training. Exposure to code may help models grasp the importance of context in communication, leading to more contextually appropriate responses. Reinforcement learning on human feedback could further refine the model's ability to generate responses that are pragmatic and contextually relevant.

Discourse Structure: Code pre-training may also impact the way LLMs understand and generate discourse structures. By learning the hierarchical nature of code syntax, models may develop a better understanding of the organization and flow of information in written and spoken language. This could lead to improvements in generating coherent and cohesive discourse in natural language.

In essence, the insights gained from the impact of code pre-training and reinforcement learning on syntax could potentially translate to improvements in other linguistic abilities such as semantics, pragmatics, and discourse structure. By exposing models to structured data and providing feedback mechanisms, LLMs may develop more robust linguistic capabilities across various linguistic domains.

How can the insights from this study be leveraged to develop LLMs that can robustly acquire and apply linguistic knowledge, beyond just performing well on in-distribution examples?

The insights from this study can be leveraged to develop LLMs that robustly acquire and apply linguistic knowledge in various ways:

Diverse Pre-training Data: Incorporating a diverse range of data sources, including code, linguistic corpora, and structured knowledge bases, can help LLMs develop a more comprehensive understanding of language. By exposing models to a variety of data types during pre-training, they can learn to generalize better across different linguistic tasks and domains.

Fine-tuning Strategies: Implementing fine-tuning strategies that focus on reinforcing syntactic, semantic, and pragmatic reasoning can help LLMs internalize linguistic knowledge more effectively. By providing targeted feedback and guidance during fine-tuning, models can improve their ability to apply linguistic principles in a wide range of contexts.

Contextual Understanding: Emphasizing the importance of context in language processing can enhance LLMs' ability to understand and generate text that is contextually appropriate. By training models to consider the broader context of a conversation or text, they can develop a deeper understanding of linguistic nuances and subtleties.

Evaluation Metrics: Developing evaluation metrics that assess not only performance on in-distribution examples but also generalization to out-of-distribution scenarios can help ensure that LLMs acquire robust linguistic knowledge. By testing models on diverse and challenging tasks, researchers can gauge the depth and breadth of their linguistic understanding.

Overall, by leveraging the insights from this study to focus on diverse pre-training data, targeted fine-tuning strategies, contextual understanding, and comprehensive evaluation metrics, developers can work towards building LLMs that excel at acquiring and applying linguistic knowledge across a wide range of linguistic tasks and challenges.